Skip To Main Content
Fusaomi Nagata on Muck Rack

Fusaomi Nagata

As seen in: MDPI
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Fusaomi

Contact Fusaomi, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Searching of Training Images with Rich Features Required for Generalization Performance of CNN Models Using Interactive Genetic Algorithms

|
By Guangsheng Shao, Fusaomi Nagata, Shiori Nakashima
| Preprints.org
You are already at the latest version Selecting training parameters for convolutional neural networks (CNNs) and determining the amount of training data required for reliable generalization remain challenging and often time-consuming tasks, typically relying on manual trial-and-error. While genetic algorithms (GAs) have been applied to hyperparameter tuning, less attention has been given to how the proportion of training data influences generalization performance.
Open in Who Shared Issue with article?

Design of Identification System Based on Machine Tools’ Sounds Using Neural Networks

|
By Fusaomi Nagata, Keigo Watanabe, Maki K. Habib, Tomoaki Morimoto
| Preprints.org
This version is not peer-reviewed. Fusaomi Nagata  *,Tomoaki Morimoto,Keigo Watanabe,Maki K. Habib Fusaomi Nagata  *,Tomoaki Morimoto,Keigo Watanabe,Maki K.
Open in Who Shared Issue with article?

MATLAB Application to Userfriendly Design Fully Convolutional Data Description Models for Defect Detection of Industrial Products and Its Concurrent Visualization

|
By Fusaomi Nagata, Shingo Sakata, Keigo Watanabe, Maki K. Habib
| Preprints.org
You are already at the latest version In this paper, fully convolutional data description (FCDD) approach is applied to the defect detection and its concurrent visualization for industrial products and materials. Our developed MATLAB application has already allowed users to efficiently and user-friendly design, train and test various kinds of neural network (NN) models for defect detection.
Open in Who Shared Issue with article?
See all 5 articles